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A Study on Action Selection Deficits in Schizophrenic Patients Using Computational Modeling in Reniforcement Learning Framework

Aghajari, Sara | 2012

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 42903 (05)
  4. University: Sharif University of Technology
  5. Department: Electrical Engineering
  6. Advisor(s): Vosoughi Vahdat, Bijan; Bahrami, Fariba
  7. Abstract:
  8. Schizophrenia is a devastating disorder that steals the opportunity of having a normal life from the sufferers. Drugs used in treating this disorder usually target psychotic symptoms and do not affect negative symptoms effectively. One of the deficits falling into this category is the impaired reversal learning. These patients usually learn the rules but do not renounce them after the contingencies of reward have been reversed. To investigate the reasons of this impairment, first a hypothesis of how different parts of the brain communicate during reversal learning is proposed using the physiological evidence. Then considering the known relation between the actor-critic model of the reinforcement learning framework and some neural substrates, this relation is translated into a mathematical language. At the final stage, the physiological changes seen in the brains of the schizophrenic patients are applied to the parameters of the model. The results indicate that the abnormalities seen in the dopaminergic system of these patients is not the cause of their inability in reversal learning, but the decreased GABAergic neurons, the dysfunctional glutamate receptors and the weak working memory are possible to mediate in such impairment. It is also suggested that if the first two of the suggested reasons are the main causes of the impairment, the agonist of the metabotropic glutamate receptors can be used as an effective drug in the treatment of the impairment.
  9. Keywords:
  10. Reinforcement Learning ; Action Selection ; Schizophernia ; Reversal Learning ; Actor-Critic Model

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